Update agent.py

agent.py

@@ -1,259 +1,223 @@
-import base64
 import os
-import io
-import contextlib
+import gradio as gr
 import requests
-from typing import TypedDict, Annotated
-from langchain_core.messages import HumanMessage, AnyMessage
-from langgraph.graph import START, StateGraph, add_messages
-from langgraph.prebuilt import ToolNode, tools_condition
-from langchain_community.tools import tool, DuckDuckGoSearchRun
-from langchain_community.document_loaders import WikipediaLoader
-from langchain_google_genai import ChatGoogleGenerativeAI
-from pathlib import Path
-import tempfile
-from dotenv import load_dotenv
-
-# constants
-API_URL = "https://agents-course-unit4-scoring.hf.space"
-QUESTIONS_URL = f"{API_URL}/questions"
-FILES_URL = f"{API_URL}/files"
-SUBMIT_URL = f"{API_URL}/submit"
-load_dotenv()
-
-class AgentState(TypedDict):
-    messages: Annotated[list[AnyMessage], add_messages]
-    file_path: str | None
-    task_id: str | None
-    url: str | None
-
-def build_gemini_llm():
-    if not os.environ.get("GOOGLE_API_KEY"):
-        raise ValueError("GOOGLE_API_KEY environment variable is not set.")
-    return ChatGoogleGenerativeAI(model = "gemini-3.7-flash", temperature = 0, max_output_tokens = 1025, include_thoughts=True)
-
-@tool
-def add_numbers(a: int, b: int) -> int:
+import inspect
+import pandas as pd
+
+import spaces
+from agent import graph
+from langchain_core.messages import HumanMessage, SystemMessage
+
+# (Keep Constants as is)
+# --- Constants ---
+DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
+
+# --- ZeroGPU Bypass ---
+# This dummy function tricks the Space into thinking we are using the GPU, preventing crashes.
+@spaces.GPU(duration=1)
+def gpu_check():
+    return "GPU available"
+
+# --- Basic Agent Definition ---
+# ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
+
+
+
+class BasicAgent:
+    def __init__(self):
+        print("BasicAgent initialized.")
+    def __call__(self, question: str) -> str:
+        response = graph.invoke({
+            "messages": [SystemMessage(content = """
+            You are a helpful assistant tasked with answering questions using a set of tools.
+Now, I will ask you a question. Report your thoughts, and answer in the fewest words possible.
+If the answer is only one word, format it as such, no extra punctuation.
+If your answer is a list, use a comma delimited list. Ex "cornstarch, freshly squeezed lemon juice, granulated sugar" or "Myanmar, Indonesia"
+If your answer is a number, only respond with the number, nothing else.
+DO NOT USE SQUARE BRACKETS IN YOUR ANSWERS UNLESS DIRECTLY STATED.
+DO NOT START YOUR ANSWER WITH "YOUR FINAL ANSWER".
+YOUR ANSWER SHOULD ONLY BE THE ANSWER AND NOTHING ELSE.
+If you need to process any video or download any content, return "I don't have enough tools yet" since such tools haven't been provided.                            
+    """), HumanMessage(content=question)],
+            "file_path": None,
+            "url": None,
+            "task_id": None
+        })
+        answer = response["messages"][-1].text
+        return answer
+
+def run_and_submit_all( profile: gr.OAuthProfile | None):
     """
-    Adds two numbers and return the result.
-    Args:
-        a (int)
-        b (int)
+    Fetches all questions, runs the BasicAgent on them, submits all answers,
+    and displays the results.
     """
-    return a + b
+    # --- Determine HF Space Runtime URL and Repo URL ---
+    space_id = os.getenv("SPACE_ID") # Get the SPACE_ID for sending link to the code
 
-@tool
-def subtract_numbers(a: int, b: int) -> int:
-    """
-    Subtracts the second number from the first and return the result.
-    Args:
-        a (int)
-        b (int)
-    """
-    return a - b
+    if profile:
+        username= f"{profile.username}"
+        print(f"User logged in: {username}")
+    else:
+        print("User not logged in.")
+        return "Please Login to Hugging Face with the button.", None
 
-@tool
-def multiply_numbers(a: int, b: int) -> int:
-    """
-    Multiplies two numbers and return the result.
-    Args:
-        a (int)
-        b (int)
-    """
-    return a * b
+    api_url = DEFAULT_API_URL
+    questions_url = f"{api_url}/questions"
+    submit_url = f"{api_url}/submit"
 
-@tool
-def divide_numbers(a: int, b: int) -> float:
-    """
-    Divides the first number by the second and return the result.
-    Args:
-        a (int)
-        b (int)
-    """
-    return a / b
-
-@tool
-def search_web(query:str) -> str:
-        """
-        Searches the web for the answer to a given question or topic.
-        Args:
-            query (str): the question or topic to search for.
-        """
-        return DuckDuckGoSearchRun()
-
-@tool
-def extract_text_from_image(img_path: str) -> str:
-    """
-    Describe the image and extract any text in it.
-    Args:
-        img_path (str): the path to the image file.
-    """
-    all_text = ""
+    # 1. Instantiate Agent ( modify this part to create your agent)
     try:
-        # Read image and encode as base64
-        with open(img_path, "rb") as image_file:
-            image_bytes = image_file.read()
-
-        image_base64 = base64.b64encode(image_bytes).decode("utf-8")
-
-        # Prepare the prompt including the base64 image data
-        message = [
-            HumanMessage(
-                content=[
-                    {
-                        "type": "text",
-                        "text": (
-                            "Describe the image and extract any text in it."
-                        ),
-                    },
-                    {
-                        "type": "image_url",
-                        "image_url": {
-                            "url": f"data:image/png;base64,{image_base64}"
-                        },
-                    },
-                ]
-            )
-        ]
-        response = model.invoke(message)
-        # Append extracted text
-        all_text += response.text + "\n\n"
-        return all_text.strip()
+        agent = BasicAgent()
     except Exception as e:
-        # A butler should handle errors gracefully
-        error_msg = f"Error extracting text: {str(e)}"
-        print(error_msg)
-        return ""
-
-@tool
-def download_and_read_file(task_id: str) -> str:
-    """
-    Download and read the file attached to the GAIA task its contents.
-    Always call this first if there is a file attached to a GAIA Task.
-    Args:
-        task_id (str): The ID of the GAIA task.
-    Returns:
-        str: The contents of the file as a string.
-    """
-
+        print(f"Error instantiating agent: {e}")
+        return f"Error initializing agent: {e}", None
+    # In the case of an app running as a hugging Face space, this link points toward your codebase ( usefull for others so please keep it public)
+    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
+    print(agent_code)
+
+    # 2. Fetch Questions
+    print(f"Fetching questions from: {questions_url}")
     try:
-        # Download the file from the GAIA API
-        response = requests.get(f"{FILES_URL}/{task_id}", timeout = 10)
+        response = requests.get(questions_url, timeout=15)
         response.raise_for_status()
+        questions_data = response.json()
+        if not questions_data:
+             print("Fetched questions list is empty.")
+             return "Fetched questions list is empty or invalid format.", None
+        print(f"Fetched {len(questions_data)} questions.")
+    except requests.exceptions.RequestException as e:
+        print(f"Error fetching questions: {e}")
+        return f"Error fetching questions: {e}", None
+    except requests.exceptions.JSONDecodeError as e:
+         print(f"Error decoding JSON response from questions endpoint: {e}")
+         print(f"Response text: {response.text[:500]}")
+         return f"Error decoding server response for questions: {e}", None
+    except Exception as e:
+        print(f"An unexpected error occurred fetching questions: {e}")
+        return f"An unexpected error occurred fetching questions: {e}", None
+
+    # 3. Run your Agent
+    results_log = []
+    answers_payload = []
+    print(f"Running agent on {len(questions_data)} questions...")
+    for item in questions_data:
+        task_id = item.get("task_id")
+        question_text = item.get("question")
+        if not task_id or question_text is None:
+            print(f"Skipping item with missing task_id or question: {item}")
+            continue
+        try:
+            submitted_answer = agent(question_text)
+            answers_payload.append({"task_id": task_id, "submitted_answer": submitted_answer})
+            results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": submitted_answer})
+        except Exception as e:
+             print(f"Error running agent on task {task_id}: {e}")
+             results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": f"AGENT ERROR: {e}"})
+
+    if not answers_payload:
+        print("Agent did not produce any answers to submit.")
+        return "Agent did not produce any answers to submit.", pd.DataFrame(results_log)
+
+    # 4. Prepare Submission 
+    submission_data = {"username": username.strip(), "agent_code": agent_code, "answers": answers_payload}
+    status_update = f"Agent finished. Submitting {len(answers_payload)} answers for user '{username}'..."
+    print(status_update)
+
+    # 5. Submit
+    print(f"Submitting {len(answers_payload)} answers to: {submit_url}")
+    try:
+        response = requests.post(submit_url, json=submission_data, timeout=60)
+        response.raise_for_status()
+        result_data = response.json()
+        final_status = (
+            f"Submission Successful!\n"
+            f"User: {result_data.get('username')}\n"
+            f"Overall Score: {result_data.get('score', 'N/A')}% "
+            f"({result_data.get('correct_count', '?')}/{result_data.get('total_attempted', '?')} correct)\n"
+            f"Message: {result_data.get('message', 'No message received.')}"
+        )
+        print("Submission successful.")
+        results_df = pd.DataFrame(results_log)
+        return final_status, results_df
+    except requests.exceptions.HTTPError as e:
+        error_detail = f"Server responded with status {e.response.status_code}."
+        try:
+            error_json = e.response.json()
+            error_detail += f" Detail: {error_json.get('detail', e.response.text)}"
+        except requests.exceptions.JSONDecodeError:
+            error_detail += f" Response: {e.response.text[:500]}"
+        status_message = f"Submission Failed: {error_detail}"
+        print(status_message)
+        results_df = pd.DataFrame(results_log)
+        return status_message, results_df
+    except requests.exceptions.Timeout:
+        status_message = "Submission Failed: The request timed out."
+        print(status_message)
+        results_df = pd.DataFrame(results_log)
+        return status_message, results_df
+    except requests.exceptions.RequestException as e:
+        status_message = f"Submission Failed: Network error - {e}"
+        print(status_message)
+        results_df = pd.DataFrame(results_log)
+        return status_message, results_df
+    except Exception as e:
+        status_message = f"An unexpected error occurred during submission: {e}"
+        print(status_message)
+        results_df = pd.DataFrame(results_log)
+        return status_message, results_df
 
-        # Determine the file type and read its contents
-        content_disposition = response.headers.get("content-disposition", "")
-        content_type = response.headers.get("content-type", "")
-        filename = None
-        if "filename=" in content_disposition:
-            filename = content_disposition.split("filename=")[1].strip('"')
-
-        if not filename:
-            filename = f"{task_id}.bin"
-
-        ext = Path(filename).suffix.lower()
-
-        if ext in(".txt", ".py", ".json", ".md", ".ymal", ".html", ".xml", ""):
-            return response.text
 
-        if ext == ".xlsx" or "xlsx" in content_type:
-            import pandas as pd
-            with tempfile.NamedTemporaryFile(suffix=".xlsx", delete=False) as file:
-                file.write(response.content)
-                temp_path = file.name
+# --- Build Gradio Interface using Blocks ---
+with gr.Blocks() as demo:
+    gr.Markdown("# Basic Agent Evaluation Runner")
+    gr.Markdown(
+        """
+        **Instructions:**
 
-            read_file = pd.read_excel(temp_path)
-            return read_file.to_string()
-        if ext == ".csv" or "csv" in content_type:
-            import pandas as pd
-            with tempfile.NamedTemporaryFile(suffix=".csv", delete=False) as file:
-                file.write(response.content)
-                temp_path = file.name
-            read_file = pd.read_csv(temp_path)
-            return read_file.to_string()
-        if ext == ".csv" or "csv" in content_type:
-                    import pandas as pd
-                    with tempfile.NamedTemporaryFile(suffix=".csv", delete=False) as file:
-                        file.write(response.content)
-                        temp_path = file.name
-                    read_file = pd.read_csv(temp_path)
-                    return read_file.to_string()
-        if ext == ".jpg" or ext == ".jpeg" or ext == ".png" or "image" in content_type:
-            from PIL import Image
-            with tempfile.NamedTemporaryFile(suffix=ext, delete=False) as file:
-                file.write(response.content)
-                temp_path = file.name
-            return extract_text_from_image(temp_path)
-        
-        # Unsupported file type 
-        return ( 
-            f"Unsupported file type: {content_type}. "
-            "I downloaded the file successfully, but I don't know "
-            "how to extract its contents." 
-            ) 
-    except requests.RequestException as e: 
-        return f"Failed to download file: {e}" 
-    except Exception as e: return f"Failed to read file: {e}"
-        
-    except Exception as e:
-        return f"error downloading or reading file: {str(e)}"
+        1.  Please clone this space, then modify the code to define your agent's logic, the tools, the necessary packages, etc ...
+        2.  Log in to your Hugging Face account using the button below. This uses your HF username for submission.
+        3.  Click 'Run Evaluation & Submit All Answers' to fetch questions, run your agent, submit answers, and see the score.
 
-    return file_content
+        ---
+        **Disclaimers:**
+        Once clicking on the "submit button, it can take quite some time ( this is the time for the agent to go through all the questions).
+        This space provides a basic setup and is intentionally sub-optimal to encourage you to develop your own, more robust solution. For instance for the delay process of the submit button, a solution could be to cache the answers and submit in a seperate action or even to answer the questions in async.
+        """
+    )
 
-@tool
-def wikipedia_search(query: str):
-    """
-    Search wikipedia for a query and return a max of three results.
-    Takes a string query as the search query
-    Args:
-        query (str): The search query.
-    """
-    try:
-        search_results = WikipediaLoader(query=query, load_max_docs=3).load()
+    gr.LoginButton()
 
-        if not search_results:
-            return f"No Wikipedia results found for {query}. Consider another query or try a web search."
-        return "\n\n---\n\n".join(
-            f"Title: {doc.metadata.get('title', 'Unknown')}\n"
-            f"Content: {doc.page_content}"
-            for doc in search_results
-        )
-    except Exception as e:
-        print(f"Wikipedia search failed for {query}: {e}")
-        return f"Wikipedia search failed for {query}. Try a web search instead."
+    run_button = gr.Button("Run Evaluation & Submit All Answers")
 
+    status_output = gr.Textbox(label="Run Status / Submission Result", lines=5, interactive=False)
+    # Removed max_rows=10 from DataFrame constructor
+    results_table = gr.DataFrame(label="Questions and Agent Answers", wrap=True)
 
-model = build_gemini_llm()
-tools = [
-    add_numbers,
-    subtract_numbers,
-    multiply_numbers,
-    divide_numbers,
-    extract_text_from_image,
-    search_web
-]
-model_with_tools = model.bind_tools(tools)
+    run_button.click(
+        fn=run_and_submit_all,
+        outputs=[status_output, results_table]
+    )
 
-def assistant(state: AgentState):
-    response = model_with_tools.invoke(state["messages"])
-    reasoning_tokens = response.usage_metadata["output_token_details"]["reasoning"]
-    print("Reasoning tokens used:", reasoning_tokens)
-    print(response.content[0].get("thinking"))
-    print("-" * 50)
-    return {
-        "messages": [response],
-        "file_path": state["file_path"],
-        "task_id": state["task_id"],
-        "url": state["url"]
-    }
+if __name__ == "__main__":
+    print("\n" + "-"*30 + " App Starting " + "-"*30)
+    # Check for SPACE_HOST and SPACE_ID at startup for information
+    space_host_startup = os.getenv("SPACE_HOST")
+    space_id_startup = os.getenv("SPACE_ID") # Get SPACE_ID at startup
 
-builder = StateGraph(AgentState)
+    if space_host_startup:
+        print(f"✅ SPACE_HOST found: {space_host_startup}")
+        print(f"   Runtime URL should be: https://{space_host_startup}.hf.space")
+    else:
+        print("ℹ️  SPACE_HOST environment variable not found (running locally?).")
 
-builder.add_node("assistant", assistant)
-builder.add_node("tools", ToolNode(tools))
+    if space_id_startup: # Print repo URLs if SPACE_ID is found
+        print(f"✅ SPACE_ID found: {space_id_startup}")
+        print(f"   Repo URL: https://huggingface.co/spaces/{space_id_startup}")
+        print(f"   Repo Tree URL: https://huggingface.co/spaces/{space_id_startup}/tree/main")
+    else:
+        print("ℹ️  SPACE_ID environment variable not found (running locally?). Repo URL cannot be determined.")
 
-builder.add_edge(START, "assistant")
-builder.add_conditional_edges("assistant", tools_condition)
-builder.add_edge("tools", "assistant")
+    print("-"*(60 + len(" App Starting ")) + "\n")
 
-graph = builder.compile()
+    print("Launching Gradio Interface for Basic Agent Evaluation...")
+    demo.launch(debug=True, share=False)